District-level monitoring of universal health coverage, India

Abstract Objective To develop a framework and index for measuring universal health coverage (UHC) at the district level in India and to assess progress towards UHC in the districts. Methods We adapted the framework of the World Health Organization and World Bank to develop a district-level UHC index (UHCd). We used routinely collected health survey and programme data in India to calculate UHCd for 687 districts from geometric means of 24 tracer indicators in five tracer domains: reproductive, maternal, newborn and child health; infectious diseases; noncommunicable diseases; service capacity and access; and financial risk protection. UHCd is on a scale of 0% to 100%, with higher scores indicating better performance. We also assessed the degree of inequality within districts using a subset of 14 tracer indicators. The disadvantaged subgroups were based on four inequality dimensions: wealth quintile, urban–rural location, religion and social group. Findings The median UHCd was 43.9% (range: 26.4 to 69.4). Substantial geographical differences existed, with districts in southern states having higher UHCd than elsewhere in India. Service coverage indicator levels were greater than 60%, except for noncommunicable diseases and for service capacity and access. Health insurance coverage was limited, with about 10% of the population facing catastrophic and impoverishing health expenditure. Substantial wealth-based disparities in UHC were seen within districts. Conclusion Our study shows that UHC can be measured at the local level and can help national and subnational government develop prioritization frameworks by identifying health-care delivery and geographic hotspots where limited progress towards UHC is being made.


Introduction
Universal health coverage (UHC) has emerged as a major goal in global health within the post-2015 millennium development agenda. 1 The objectives of UHC are typically defined by three dimensions: the population that is covered by pooled funds; the proportion of direct health costs covered by pooled funds; and the health services covered by those funds. 2While these dimensions provide a framework for a country's aspirations for its health system, the path to UHC varies from one country to another.Over the past four decades, many low-and middle-income countries, including India, have implemented health-sector reforms in pursuit of UHC.Most of these reforms have embraced some degree of health system decentralization, primarily due to the adoption of decentralized governance in public services. 35][6] In most decentralized governance systems, the responsibility of health-care delivery is shared between the national and provincial (or state) governments, forming a two-tier system. 7[10][11][12][13][14][15][16][17] What is relatively uncommon is a three-tier arrangement, where exclusive power is granted to local governments below the provincial (state) government. 18The complexity of threetier decentralization provides a compelling rationale for parallel geographic disaggregation of the UHC index.This approach aims to track progress in greater detail, acknowledging the distinct challenges and dynamics at each level of governance.
Moreover, the resurgence of interest in Alma Ata principles, which emphasize the importance of local health systems as an essential element to achieve UHC, 19 calls for extending metrics to the local level to gain insight on how a country can move its health system towards UHC more equitably.
Many low-and middle-income countries, for example Argentina, Brazil, 20 India, 6 Indonesia, 7 Nigeria, 20 Pakistan, 21 Philippines, 22 South Africa 23 and Uganda, 22 have adopted a three-tier decentralized system.Each of these countries has, to varying degrees, decentralized the design and implementation of UHC programmes to the third tier, namely local governments.Of these countries, India has a unique position as it accounts for a sixth of the world's population, a fifth of the global burden of disease and a quarter of all households facing catastrophic health expenditure.As a result, it is perhaps the country for which attaining sustainable development goal (SDG) 3.8, i.e. achieving UHC, is particularly important. 24dditionally, districts, which constitute local governments in India, have an average population of about 1.9 million. 24These factors make India well-suited to take UHC metrics to the local government level to support more precise decision-making.
While the Indian government has created composite measures to track health-system performance at the state level, we lack comprehensive district-level measures, which is a challenge for decentralization efforts.Identifying which districts are closest or furthest from achieving UHC is difficult without district-level UHC measures.In addition, given the multidimensional nature of UHC which spans health-care services and financial risk protection, such an index can identify where a district must focus its efforts.Thus, a district-level UHC index, which we call UHC d , has the potential to identify geographic hotspots of low UHC, help design appropriate district-level programmes, and enable progress in UHC in districts to be explicitly linked to public expenditure and outcomes.
The aim of this study was to outline a framework for measuring UHC at the district level in India and provide an adaptable method to monitor progress at local levels in three-tier decentralized systems.

Study design
Among the various UHC measurement frameworks available, we chose to base our UHC index on the framework developed by the World Health Organization (WHO) and the World Bank. 25This framework, which is used to produce the global monitoring reports that track UHC progress in 183 countries, follows methods proposed in 2018. 8We adapted this framework to the district level and used routinely collected health surveys and programme data in India to compare the district health systems.
Following the WHO and World Bank terminology, UHC d has five tracer domains: reproductive, maternal, newborn and child health; infectious diseases; noncommunicable diseases; service capacity and access; and financial risk protection.In line with earlier research, we combined service coverage and financial protection into one measure to capture both the unmet need for services and the resultant economic burden of unmet needs. 10,26

Variables
Our UHC index contains a wider set of indicators than the core indicators in the WHO and World Bank framework.Thirteen of the 24 tracer indicators are based on the WHO and World Bank framework, while we included 11 additional variables to capture districtlevel UHC priorities in India (Table 1).
From the original set of WHO and World Bank indicators, we measured five in an identical way.We measured the rest of the indicators differently given data constraints and relevance (Table 1).We split health workforce into two tracer indicators (Table 1), unlike the WHO and World Bank framework.The detailed list of tracer indicators, related changes, estimation method and the rationale are summarized in the technical report available in the online repository. 30Where data were unavailable, we used proxy indicators, especially for noncommunicable diseases and service capacity and access (online repository). 30To allow stable estimation at the district level, we excluded four tracer indicators used in the original WHO and World Bank framework: malaria prevention; treatment of human immunodeficiency virus; care seeking for symptoms of pneumonia; and health security, due to the lack of data in the surveys we reviewed.

Data sources and measurement
We primarily used four publicly available secondary data sources.Most indicators in the domains of reproductive, maternal, newborn and child health, infectious diseases, and noncommunicable diseases (except tuberculosis treatment) were drawn from the fifth round of the National Family and Health Survey (2019-2021). 27This survey provides district-level estimates for family planning, reproductive and child health, and noncommunicable diseases for all 707 districts and 36 states and union territories of India.The district-level estimates of tuberculosis treatment success rates were from the 2014 annual tuberculosis report. 28We took information on service capacity and access from the population-linked facility module of the District-Level Household Survey (2012-2013). 29This survey contains information on human resources, infrastructure and services for a sample of subcentres and primary health-care centres designed to be representative at the district level and a census of community health centres, and district and subdistrict hospitals.Although the information is from 2012-2013, this survey provides the most recent district-level facility data.We focused only on primary health-care centres and community health centres since they serve as the people's first point of contact with qualified doctors in the districts.
In addition, instead of using the WHO benchmarks and list of core medicines to estimate indicators related to service capacity and access, we used the minimum thresholds set by the Indian Public Health Standards (online repository). 30ata for catastrophic health expenditure and impoverishment came from the 75th round of the health survey of the National Sample Survey Office, 31 which has nationally representative health expenditure data.Our choice of data sources reflects: (i) district representability; (ii) high response rates; and (iii) availability of multiple measures relevant to UHC d .We transformed all tracer indicators such that 0 indicates no coverage while 100 indicates complete coverage for the population.For financial risk protection, we took the complement of the incidence of impoverishment and catastrophic health expenditure, i.e. the percentage of the population not incurring catastrophic and impoverishing payments to match the directionality of UHC d .
To maximize geographical coverage, we applied imputation techniques to generate estimates for districts with missing data.When faced with missing data due to geographic redistricting over time, we followed the imputation method in a 2021 publication. 32We used the STATA (StataCorp.LP, College Station, United States of America) hot-deck imputation algorithm 33 for districts lacking data on indicators of service capacity and access.Additionally, we used regression imputation 34 to estimate values for catastrophic health expenditure and impoverishment for all districts in the data set (online repository). 30Consequently, the UHC d was constructed for 687 of 707 districts across India.

Index construction
We used geometric means to aggregate tracer indicators, ensuring equal weighting for each tracer indicator.This method, chosen for its resilience to extreme values, is consistent with established practices. 8,10,14,17To calculate UHC d , we initially aggregated within a tracer area by calculating the geometric mean of the constituent tracer indicators.Then, we aggregated within a tracer domain by calculating the geometric mean of the constituent tracer areas.Finally, we derived the UHC d for a district by calculating the geometric mean across the five tracer domains (Fig. 1

Research
journals/ bulletin).Additionally, UHC d can be disaggregated into its two key components: (i) service coverage, calculated as the geometric mean of reproductive, maternal, newborn and child health, infectious diseases, noncommunicable diseases, service capacity and access; and (ii) financial risk protection.Since we measured all tracer indicators on a scale of 0% to 100%, the UHC d is on the same scale, with higher scores indicating better performance.

Statistical analysis
Districts with comparable (high or low) UHC d were clustered together in certain states.We used a multilevel model with only intercept and state effects to quantify UHC d variance between and within states.Additionally, in low-and middleincome countries, studies have shown that higher levels of UHC are associated with lower levels of poverty. 15,17,35In our study, we further investigated the relationship between UHC and poverty by using the multidimensional poverty index (online repository). 30,36Unlike conventional income-based measures, the multidimensional poverty index captures a slightly overlapping but largely distinct aspect of poverty, that is, it identifies non-income factors associated with social inequalities. 37Moreover, this index is also a recognized metric in India for reporting on poverty.

Equity
We also summarized the degree of inequality within districts using a subset of 14 tracer indicators (online repository). 30Following a 2018 study, 8 we compared the UHC d of the district population to that of the historically disadvantaged population groups.This comparison ensured consistency with the SDG focus on national coverage and coverage in the most disadvantaged groups. 8The disadvantaged population subgroups were based on four key inequality dimensions: wealth quintile, urban-rural location, religion and social group.Specifically, for each district, we calculated the ratio of the geometric mean of the tracer indicators for the disadvantaged subgroup to the geometric mean for the district.We then multiplied this ratio with UHC d , as calculated for the district, to give the value of UHC d for the subgroup.

Sensitivity analysis
To test the robustness of our results, we recalculated and ranked the districts using different alternative approaches.These approaches included: adjusting the UHC d for inequality in intervention coverage between the poor and better-off districts by switching from the population mean to the so-called achievement index; 38 recalculating UHC d using arithmetic means and overall geometric mean instead; and assessing the index's sensitivity to the choice of tracer indicators by dropping indicators one at a time.We determined sensitivity by estimating the Spearman rank correlation between the original district rankings and the rankings based on alternative approaches.Finally, using state-level rural health statistics of 2020-2021 instead of the district-specific data from the District-Level Household Survey (2012-2013), we confirmed the robustness of our findings.

Results
Overall, 97.2% (687/707) of the districts had information on all 24 tracer indicators.The median value of UHC d was 44.0% (Table 2), ranging from 26.4% in Baksa (Assam) to 69.4% in the Nilgiris (Tamil Nadu).Only 21.1% (145/687) of the districts had a UHC d value greater than 50.0%(online repository). 30The districts had a median service coverage of 40.2% and 66.6% for financial risk protection (Table 2).We categorized the districts into high, medium and low UHC d values and calculated the median values for UHC d and other tracer domains by terciles (online repository). 30e also disaggregated UHC d by tracer areas and tracer indicators (online repository). 30Infectious diseases had the highest median coverage (82.8%; range: 8.8 to 97.8) across all tracer domains.Within infectious diseases, coverage for tuberculosis treatment was 88.0%; and water, sanitation and hygiene was 79.4%.Noncommunicable diseases had the lowest coverage (9.8%; range: 4.3 to 37.1), with more than half of the districts having a coverage of only 5.0% to 10.0%.Cancer screening coverage was poor (< 1.0%).However, coverage for prevention of hypertension (63.2%) and diabetes treatment (92.6%) and reduction in tobacco use (92.6%) was high at the district level.
The median reproductive, maternal, newborn and child health coverage was 64.8%, ranging from 18.8% to 86.0%.Within this domain, family planning had the least coverage (55.6%), followed by immunization (63.4%), while coverage of delivery care was 80.1%.The overall service capacity and access availability was 54.9%, ranging from 9.5% to 92.6%, with most of its tracer areas performing poorly.Only 48.7% of the district populations had access to round-the-clock health facilities.UHC d for availability of health personnel was 49.4%, and for availability of essential medicines it was 60.0%.Maternal and child health training for health personnel at the district level was only 48.0%.Significant spatial differences in UHC d existed across states and regions in India (online repository). 30Districts in southern states such as Andhra Pradesh, Kerala, Puducherry and Tamil Nadu had higher coverage (> 50%) compared with the relatively less-developed states from the central, eastern and north-eastern regions such as Bihar, Jharkhand, Manipur and Uttar Pradesh (< 40%).Distribution based on level of UHC d (low, medium or high) showed similar findings (online repository). 30ost high-performing districts were from the southern states of Andhra Pradesh, Kerala and Tamil Nadu, and all districts in Andhra Pradesh and Tamil Nadu were high performers; in fact, the top three performing districts were from Tamil Nadu (Table 3).On the other hand, many districts from central and eastern states such as Bihar, Jharkhand and Uttar Pradesh were poor performers.All districts in Manipur and 89.3% (67/75) of the districts in Uttar Pradesh were low performers.Eastern states, such as Bihar, Jharkhand and Odisha, also had a considerable number of low performers.A few states, such as Chhattisgarh, Madhya Pradesh and Meghalaya, had a mix of well and poorly performing districts.
Most variation (69.4%) in UHC d was due to differences between states (Table 4).The remaining 30.6% was attributable to differences between districts (within-state).UHC d levels were most homogenous in Sikkim (coefficient of variation < 5.0%), while both Kerala and Puducherry had the highest withinstate heterogeneity in UHC d (coefficient of variation > 12.0% to 14.0%; online repository). 30The correlational analysis between UHC d and the multidimensional poverty index showed a significant negative correlation (coefficient of variation −0.51, P < 0.05; Fig. 2).
Our equity assessment showed that progress in UHC d was unequal across the different subgroups (online repository). 30Wealth-related disparity was the most notable compared with other dimensions.Across the 687 districts, the difference in UHC d between the poorest quintile and the district population varied from −3.0% to 24.0%.The median UHC d was lower in the poorest wealth quintile (36.9%) than the district population (43.9%).The UHC d of subgroups also varied considerably between districts across states.
The UHC d was stable in various sensitivity tests (online repository). 30he district rankings were not sensitive to inequality adjustments (correlation coefficient, ρ: 0.9916; P < 0.0001).

Research
The biggest effect in terms of district rankings was switching to the arithmetic mean as the choice of summary method (ρ: 0.8584; P < 0.0001).Comparatively, the changes in the rankings were minimal because of using an overall geometric mean method (ρ: 0•9614; P < 0.0001).As regards sensitivity of the index to the choice of tracer indicator, the Spearman rank correlation was high across all permutations (lowest ρ: 0.95).The most pronounced effect on the rankings was when we dropped indicators on cancer screening and health insurance, compared with other indicators.Lastly, we saw a high correlation between recent state-level data and older district-level data on service capacity and access (online repository). 30

Discussion
0][41] Using the WHO and World Bank framework, we developed a district-level UHC index tailored to India's UHC priorities and context, primarily using nationally representative surveys.This index provides a detailed measure of local UHC that is closer to policy and programme implementation and yet is within a common empirical structure to support comparison across place and time.The index reflects recent research that emphasizes the importance of examining subnational variation in health progress. 14,42,43Our index also extends the relevance of UHC metrics to encompass three-tier governance systems seen in several low-and middle-income countries.Thus, our proposed UHC framework represents a decentralized and peoplecentred approach to UHC.Anchored in the WHO and World Bank framework, the index offers a well-defined yet flexible approach for measuring UHC, combining globally consistent and nationally focused data sources.
Our proof-of-concept analysis suggests that UHC varies widely in India: southern districts are, on average, closer to attaining UHC than elsewhere in India.Overall, the majority of Indian districts were unable to reach 50.0% on the UHC index.Service coverage indicator levels were higher than 60.0%, except those indicators related to noncommunicable diseases, and service capacity and access.Health insurance coverage was limited, with about one in 10 people experiencing catastrophic and impoverishing health expenditure.We found evidence of substantial wealth-related disparity in UHC within districts across states, indicating a strong link between poverty and progress towards UHC.Ac-cordingly, different governance, disease incidence, 44 and social, cultural and political background 45 imply that India's pathways to UHC will be largely shaped at the state and district level.Recent research, facilitated by the availability of district-level data in India, highlights the importance of local-level analyses of health outcomes and services, such as in cancer screening, 46 caesarean sections, 47 hysterectomies 48 and health insurance. 49ith countries adopting UHC as an aspiration for their national policy, studies of several countries have shown varying levels of UHC and different pathways towards achieving UHC. 2 Preliminary evidence suggests that the challenges to achieving nationwide UHC will depend on tackling inequities and variation in service coverage and financial risk protection within the country. 14,50At a practical level, locallevel data and tracking are also essential to inform decision-making and build responsive and resilient health systems.Our UHC d, provides a single metric for the district-level administration in India, fosters transparent tracking, and enables prioritization of local strategies using publicly available data.
While our conceptualization of UHC d is easy to implement and facilitates time and cross-district comparisons, certain limitations must be addressed for its continued use.First, since the index is based on routinely collected national surveys -which are not designed with a comprehensive UHC metric in mind -inaccuracies or inconsistencies may emerge over time.These issues may arise due to changing definitions as well as changing needs of what UHC should measure; for example, any UHC metric reflects the health system's choices on screening and treatment.Deciding where on the path to UHC delivery of care versus a public health function of screening for prevention should be incorporated is important and will need more detailed data and debate. 8This need suggests the importance of advocating for data specifically focused on UHC within and across settings.Availability of such data will address the immediate constraints of this analysis, including the reliance on older data for certain indicators and the broader challenge of insufficient district-level health survey and administrative data.Another limitation stems from the lack of district-specific health conditions that are present locally, as we  used a nationwide data set.Finally, while our approach supports equity-related analysis, the index does not directly incorporate equity and may understate the strong link between inequality and UHC that we report.Our work presents a proof-ofconcept of how UHC can be measured at the local level of policy implementation rather than at country or policy-design levels.The UHC d is also a powerful means to identify issues for further, context-specific study.For example, we used the index to identify a range of districts that would be suitable for qualitative research with district-level health system actors as part of the Lancet Citizens' Commission on re-imagining India's health system.Such research offers the advantage of showing where and how progress towards UHC may be achieved.We believe this research can serve as a starting point for identifying key local policy actions to guide an intensified effort towards UHC over the next decade.
Resultados El promedio de CSU d fue del 43,9% (rango: 26,4 a 69,4).Existían diferencias geográficas significativas: los distritos de los estados del sur tenían un CSU d más alto que los del resto de India.Los niveles de los indicadores de cobertura de servicios eran superiores al 60%, excepto para las enfermedades no transmisibles y para la capacidad y el acceso a los servicios.La cobertura del seguro sanitario era limitada, y alrededor del 10% de la población se enfrentaba a gastos sanitarios catastróficos y empobrecedores.Se observaron disparidades significativas en la CSU según el nivel de riqueza dentro de los distritos.Conclusión Este estudio demuestra que la CSU puede medirse a escala local y puede ayudar a los gobiernos nacionales y subnacionales a desarrollar marcos de priorización mediante la identificación de los puntos críticos geográficos y de prestación de asistencia sanitaria en los que los avances hacia la CSU son limitados.where PC is pregnancy care, FP is family planning and FI is full immunization.
PC contains four indicators and is calculated as: where ANC is antenatal care, PNC is postnatal care, SBA is skilled birth assistance and DI is delivery in an institution.calculated as: where W is water, sanitation and hygiene and TB is tuberculosis.
W contains two indicators and is calculated as: where IW is access to improved water and IS is access to improved sanitation.
Service coverage is calculated as: 4 √(R x I x N x S) UHC index is calculated as: 5 √(R x I x N x S x F) calculated as: where BP is normal blood pressure, BS is normal blood sugar, BC is breast cancer screening, CC is cervical cancer screening and T is tobacco non-use.where HFA is health facility access, beds is health infrastructure, EM is essential medicine, HW is workforce and MCH is maternal and child health.
HW contains two indicators and is calculated as: where D is doctors and PS is paramedical staff.
M contains two indicators and is calculated as: where

Fig. 2 .
Fig. 2. Correlation between district-level universal health coverage index and multidimensional poverty index at the district level, India

calculated as: 5 √
(HFA x beds x EM x HW x MCH)

Table 4 . Null model with only state-level effects for district-level universal health coverage index, India
CI: confidence interval; SE: standard error.
BE is basic emergency obstetric care training and SBT is skilled birth assistance training.
UHC: universal health coverage.Note: variables are explained in Table1.